Tendon-driven Continuum Robots (TDCRs) excel at operating in confined and complex environments due to their inherent flexibility and high degrees of freedom. Despite these advantages, achieving precise control in multi-section TDCRs remains challenging because of their infinite degrees of freedom, nonlinear behaviour, and coupled tendon actuation. This paper introduces a generalised virtual actuation space framework that significantly simplifies kinematic modelling and control of multi-section TDCRs. By formulating a novel concept of virtual actuation space that converts each bending section of the TDCR two-input actuation system, the proposed method reduces the complexity of TDCR modelling and control, enabling unique and simplified representations of each section. Furthermore, the formulation decouples each section via a generalised tendon mapping matrix that handles coupling effects. Experimental validation on a two-section TDCR demonstrates tracking performance with a root mean square error below 3 mm, corresponding to approximately 1% of the robot’s length. These results confirm the method’s efficacy in improving TDCR precision for minimally invasive surgery, industrial applications, and other constrained environments.

During the COVID-19 lockdown, Latin American artists turned low-cost, open platforms such as Mozilla Hubs, Twitch, Jitsi, YouTube and OBS into ephemeral infrastructures for collaborative VR concerts, 3D live-coding sessions, collective streaming events and networked Algoraves. Drawing on participant observation, event documentation and informal interviews, this article examines these cases with a focus on La Fábrica VR (TOPLAP México) and related experiences in Mexico, Peru, Colombia, Argentina and Costa Rica. We argue that these practices materialise a situated, collaborative and tactically informal technological production that reconfigures agency, embodiment and listening in immersive environments.

The concentric cable-driven manipulator (CCDM) is slender enough to work in narrow oral environments. However, the configuration detection of the CCDM with two segments is the key issue to realize the servo control and force sensing. In this paper, a Segmented Self-Organizing Map (S-SOM) method is presented for configuration detection of the CCDM without preset markers. Firstly, two-dimensional point cloud of the CCDM is acquired by the binocular vision. Secondly, the dimension reduction of point cloud is realized by principal component analysis to avoid noises and eliminate redundant information. Then, the threshold is set to distinguish the segment with different diameters that need to be trained independently. Thirdly, quantization errors and topological errors are designed to select different training parameters of the CCDM with two segments. Finally, the experimental results show that the average point-to-point distance error is less than 2.57 mm. The effectiveness of the proposed S-SOM method for configuration detection of the CCDM is verified.

This paper proposes an indoor LiDAR-visual fusion framework based on dynamic elimination. The framework effectively suppresses transient dynamic interferences. It integrates multi-view geometric constraints with semantic priors, which is achieved through a prior model-guided dynamic feature elimination strategy. Moreover, an adaptive gamma correction (AGC) algorithm enhances the image quality under uneven illumination. Meanwhile, an improved quadtree algorithm homogenizes feature point distribution. These two measures achieve more robust ORB feature extraction while ensuring image quality. For map fusion, we focus on combining the processed visual map with the LiDAR map. The visual map, with dynamic feature points eliminated, is first aligned with the LiDAR map via Harris corner detection. We then integrate them using Bayesian inference-based grid information fusion to generate the final integrated map. Extensive evaluations were carried out on six dynamic sequence datasets, and practical dynamic corridor mapping experiments were also conducted. These evaluations and experiments demonstrate the superiority of the proposed system.

This article explores new approaches for notating the morphology of sound using the framework of CS Peirce’s concept of indexical signs. While pictographic and symbolic notation struggle to notate the lived dynamic experience of music, indexical notation offers new possibilities for attending to the material, temporal and spectral flux of sound. Drawing from theories outlined by Floris Schuiling, this article presents a case study of an interactive score titled Undersong1 for solo performer that eschews symbolic or pictographic notation in favour of establishing indexical causal relationships between performer and visual responses. The case study suggests that indexical signs may offer an accessible way to engage performers with spectral and morphological elements of sound, opening new pathways for notation to engage experiential phenomena.

A new breed of prophets – intermediaries and pastoral bros of an AI industry with metaphysical aspirations – has surfaced on the global stage during troubled times. They make great promises, offer predictions and warnings, and stake out directions for humanity. This article argues that they do so by invoking the implicit collective memory of the apocalyptic imaginary known from ancient Jewish apocalyptic writings and, more specifically, by reenacting what we call prophetic memory. Through close readings in the tradition of biblical exegesis coupled with philosophical and critical hermeneutics, we trace strong AI narratives of doom and salvation to a range of media forms such as Twitter/X postings, books, interviews, journalistic feature articles, and reporting. Through these media, AI prophets speak of the end times while simultaneously offering a new beginning for humankind, not unlike the ancient prophets of the Hebrew Bible. Prophetic memory, we submit, is furthermore a mode of ‘collective future thought’ and an instantiation of the ‘remembering-imagining-system’. While its purpose is to create stability for a particular vision for the future, there is also a productive ambivalence of order and disorder at work within the apocalyptic AI imaginary. To question this ambiguous yet extremely powerful fixture on the human horizon, there is a need, we argue, for bothering the political-religious dimensions of the hegemonic AI imaginary and for scrutinizing how the AI industry founds its power base on the clout of prophetic memory – in a time of crisis in which many look for guidance and direction.

NEURAL MATERIALS (2024) is a live AV show created by SONAMB (Vicky Clarke). The project represents a collaboration between Vicky Clarke, visual artist Sean Clarke, and industry partner Bela, a company specialising in hardware with interactive sensors for music-making. The AV show utilises a new performance system incorporating a hybrid set-up in combination with both a sound sculpture and the output of a machine learning model trained on a ‘post-industrial’ sonic dataset. The dataset renders in sound Manchester’s industrial past and present through field recordings of cotton mills, the canal network and the electromagnetic resonances of a newly gentrified city centre. This article analyses NEURAL MATERIALS as musical composition, live AV show and a demonstration of creative audio-generative AI, linking the work to scholarly and compositional legacies of Sonic Materialism and musique concrète. By combining documentation analysis and performance analysis, I interrogate how sound’s indexical properties are transformed via machine learning (ML) processes, questioning whether machines are able to evoke a sense of space or heritage. Ultimately, I contend that such audio-generative systems have the capacity to reshape our perception of industrial histories, technologies and future sonic realities, indexing sociohistorical cues that are reactivated at the point of listening.

Generative soundscapes in exhibition spaces offer new possibilities for integrating artistic practice, technological innovation and perceptual experience. Contemporary tools – including stochastic algorithms, random oscillators and diverse methods of sound synthesis – enable the construction of environments that respond dynamically to external conditions. With the integration of artificial intelligence and machine learning, such systems acquire additional flexibility: they are able to register the presence and movement of visitors, evaluate changes in audience density and adjust to the acoustic properties of the space in real time. As a result, sound layers can emerge when a participant approaches, the balance of elements may shift with fluctuations in the crowd, and potential peaks in volume can be anticipated and mitigated. In this way, a fixed soundtrack is transformed into an adaptive system, where the exhibition environment functions as an active, responsive organism. Sound ceases to serve merely as a background and becomes a structural component that directly influences how the artistic work is perceived.

Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.